About the Company
We are a next-generation AI consulting, strategy, and engineering practice focused on transforming enterprises into Agentic AI-powered organizations intelligent, autonomous, and deeply data-driven. Data is the fuel of agentic AI. Without high-quality, mapped, accessible, governed data, no AI agent can perform reliably.
About the Role
The AI Data Strategist plays a critical role in shaping enterprise data readiness, ensuring the data foundations required for agentic AI workflows, reasoning, decisioning, and orchestration are in place. You are the bridge between business processes, data ecosystems, AI models, and agentic systems.
Responsibilities
- Own data readiness and enterprise data mapping for all agentic AI solutions. Ensure the right data is available, accessible, trustworthy, and structured to power reasoning, RAG, agent workflows, and AI decisioning.
- Lead Enterprise Data Readiness Assessment
- Evaluate current-state data maturity across
- Completeness
- Accuracy
- Timeliness
- Lineage
- Ownership
- Accessibility
- Compliance
- Assess readiness of structured, semi-structured, and unstructured data.
- Identify data gaps that block AI agent performance (missed fields, inconsistent labels, missing documents, poor metadata, etc.).
- Evaluate data availability across core platforms (CRM, ERP, HRMS, core banking, SCM, procurement, etc.).
- Drive Process-to-Data Mapping
- Work with Functional Design Leads to translate workflows into data needs.
- Identify what data each agent requires for:
- Context
- Reasoning
- RAG recall
- Decision logic
- Exception handling
- Build Data Dependency Maps linking:
- Process Tasks Data Elements Source Systems
- Create Data Blueprints for each agent or workflow.
- Define Agentic AI Data Architecture Requirements
- Work with Technical Pod Lead and Data Engineering teams to:
- Determine required ingestion pipelines
- Identify vectorization opportunities for RAG
- Define semantic layers for reasoning
- Recommend transformations for agentic workflows
- Define context windows, embeddings, retrieval needs, memory systems, and data enrichment requirements.
- Partner with Data Engineering & Platform Teams
- Translate data requirements into ingestion, transformation, and storage specifications.
- Validate feasibility of data access and integration.
- Support creation of pipelines for structured data, documents, logs, and knowledge repositories.
- Align with MLOps/LLMOps for:
- Data refresh cycles
- Indexing
- Incremental updates
- Perform Data Gap & Quality Analysis
- Conduct data profiling and metadata discovery.
- Identify issues affecting agent accuracy (duplicates, missing values, inconsistent formats, non-standard taxonomies).
- Recommend data cleansing, enrichment, remediation, or restructuring.
- Work with data governance teams to define ownership and ongoing maintenance.
- Ensure Compliance & Responsible Data Practices
- Validate data access risk, privacy constraints, and PII exposure.
- Collaborate with CoE governance for ethical AI usage.
- Define secure access patterns and masking requirements for AI workloads.
- Support Pod Teams During Build
- Provide ongoing data clarifications for FDEs and technical teams.
- Validate that prototypes and agents are consuming the correct data.
- Troubleshoot issues related to data retrieval, latency, and context accuracy.
- Ensure data quality supports performance KPIs.
- Contribute to Practice IP & Data Frameworks
- Create reusable:
- Data readiness templates
- Data maps and catalogs
- Data requirement questionnaires
- Semantic mapping guides
- RAG data preparation patterns
- Help institutionalize Agentic AI Data Methodology across the practice.
Qualifications
- 815 years in data strategy, data architecture, analytics consulting, or enterprise data transformation.
- Strong background in data discovery, data quality, data modeling, metadata management, and data governance.
- Experience in digital transformation or AI/ML-focused programs.
- Exposure to industry data models (Banking, Retail, Manufacturing, Supply Chain, HR, etc.).
- Prior consulting experience strongly preferred.
Required Skills
- Structured problem-solving and analytical thinking
- Deep understanding of enterprise systems and data sources
- Ability to simplify complex data ecosystems
- Strong documentation and visualization capability
- Good knowledge of LLM/RAG patterns and data requirements
- Ability to collaborate across business, tech, and governance stakeholders
- Understanding of data privacy, security, and responsible AI compliance
Preferred Skills
- Certifications in Data Governance, Data Strategy, Cloud Data Platforms, or Data Architecture preferred.
